Theoretical and Applied Genetics

, Volume 126, Issue 3, pp 747–761 | Cite as

Multi-environment analysis and improved mapping of a yield-related QTL on chromosome 3B of wheat

  • Julien Bonneau
  • Julian Taylor
  • Boris Parent
  • Dion Bennett
  • Matthew Reynolds
  • Catherine Feuillet
  • Peter Langridge
  • Diane Mather
Original Paper

Abstract

Improved mapping, multi-environment quantitative trait loci (QTL) analysis and dissection of allelic effects were used to define a QTL associated with grain yield, thousand grain weight and early vigour on chromosome 3BL of bread wheat (Triticum aestivum L.) under abiotic stresses. The QTL had pleiotropic effects and showed QTL x environment interactions across 21 diverse environments in Australia and Mexico. The occurrence and the severity of water deficit combined with high temperatures during the growing season affected the responsiveness of this QTL, resulting in a reversal in the direction of allelic effects. The influence of this QTL can be substantial, with the allele from one parent (RAC875) increasing grain yield by up to 12.5 % (particularly in environments where both heat and drought stress occurred) and the allele from the other parent (Kukri) increasing grain yield by up to 9 % in favourable environments. With the application of additional markers and the genotyping of additional recombinant inbred lines, the genetic map in the QTL region was refined to provide a basis for future positional cloning.

Notes

Acknowledgments

The authors thank Ali Izanloo for field data; Eugenio Perez, Araceli Torres and other members of the CIMMYT physiology group for data collection; Delphine Fleury and other members of the ACPFG for advice and technical support; and Pierre Sourdille (INRA Clermont-Ferrand) for marker sequences. The work was supported through funding from the Grain Research and Development Corporation, the Australian Research Council, the Government of South Australia and the University of Adelaide.

Supplementary material

122_2012_2015_MOESM1_ESM.pdf (100 kb)
Online Resource 1 (PDF 101 kb)
122_2012_2015_MOESM2_ESM.pdf (295 kb)
Online Resource 2 (PDF 294 kb)
122_2012_2015_MOESM3_ESM.pdf (218 kb)
Online Resource 3 (PDF 217 kb)
122_2012_2015_MOESM4_ESM.pdf (118 kb)
Online Resource 4 (PDF 117 kb)
122_2012_2015_MOESM5_ESM.pdf (219 kb)
Online Resource 5 (PDF 219 kb)

References

  1. Alexander LM, Kirigwi FM, Fritz AK, Fellers JP (2012) Mapping and quantitative trait loci analysis of drought tolerance in a spring wheat population using amplified fragment length polymorphism and Diversity Array Technology markers. Crop Sci 52:253–261CrossRefGoogle Scholar
  2. Beales J, Turner A, Griffths S, Snape JW, Laurie DA (2007) A pseudo-response regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat (Triticum aestivum L.). Theor Appl Genet 115:721–733PubMedCrossRefGoogle Scholar
  3. Bennett D, Reynolds M, Mullan D, Izanloo A, Kuchel H, Langridge P, Schnurbusch T (2012a) Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments. Theor Appl Genet. doi: 10.1007/s00122-012-1927-2 Google Scholar
  4. Bennett D, Izanloo A, Reynolds M, Kuchel H, Langridge P, Schnurbusch T (2012b) Genetic dissection of grain yield and physical grain quality in bread wheat (Triticum aestivum L.) under water-limited environments. Theor Appl Genet 125:255–271PubMedCrossRefGoogle Scholar
  5. Bennett D, Izanloo A, Edwards J, Kuchel H, Chalmers K, Tester M, Reynolds M, Schnurbusch T, Langridge P (2012c) Identification of novel quantitative trait loci for days to ear emergence and flag leaf glaucousness in a bread wheat (Triticum aestivum L.) population adapted to southern Australian conditions. Theor Appl Genet 124:697–711PubMedCrossRefGoogle Scholar
  6. Broman K, Wu H with ideas from Gary Churchill SS; Contributions from Brian Yandell (2010) qtl: tools for analysing QTL experiments. R package version 1.15-15Google Scholar
  7. Butler D, Cullis B, Gilmour A, Gogel B (2009) ASReml-R, reference manual. Technical report, Queensland Department of Primary IndustriesGoogle Scholar
  8. Chen G, Krugman T, Fahima T, Chen K, Hu Y, Röder M, Nevo E, Korol A (2010) Chromosomal regions controlling seedling drought resistance in Israeli wild barley Hordeum spontaneum C. Koch. Genet Resour Crop Evol 57:85–99CrossRefGoogle Scholar
  9. Cone KC, McMullen MD, Bi IV, Davis GL, Yim Y-S, Gardiner JM, Polacco ML, Sanchez-Villeda H, Fang Z, Schroeder SG, Havermann SA, Bowers JE, Paterson AH, Soderlund CA, Engler FW, Wing RA, Coe EH (2002) Genetic, physical, and informatics resources for maize. On the road to an integrated map. Plant Physiol 130:1598–1605PubMedCrossRefGoogle Scholar
  10. Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11:381–393CrossRefGoogle Scholar
  11. Deckers J, Verhulst N, Govaerts B (2009) Classification of the soil at CIMMYT’s experimental station in the Yaqui Valley near Ciudad Obregon, Sonora, Mexico. CIMMYT, Mexico, DFGoogle Scholar
  12. Diab AA, Kantety RV, Ozturk NZ, Benscher D, Nachit MM, Sorrells ME (2008) Drought-inducible genes and differentially expressed sequence tags associated with components of drought tolerance in durum wheat. Sci Res Essays 3:9–26Google Scholar
  13. Fleury D, Jefferies S, Kuchel H, Langridge P (2010) Genetic and genomic tools to improve drought tolerance in wheat. J Exp Bot 61:3211–3222PubMedCrossRefGoogle Scholar
  14. Hayden MJ, Nguyen TM, Waterman A, Chalmers KJ (2008) Multiplex-ready PCR: a new method for multiplexed SSR and SNP genotyping. BMC Genomics 9:80PubMedCrossRefGoogle Scholar
  15. Holland JB (2007) Genetic architecture of complex traits in plants. Curr Opin Plant Biol 10:156–161PubMedCrossRefGoogle Scholar
  16. Hunt JR, Kirkegaard JA (2011) Re-evaluating the contribution of summer fallow rain to wheat yield in southern Australia. Crop Pasture Sci 62:915–929CrossRefGoogle Scholar
  17. Jongdee B, Fukai S, Cooper M (2002) Leaf water potential and osmotic adjustment as physiological traits to improve drought tolerance in rice. Field Crop Res 76:153–163CrossRefGoogle Scholar
  18. Kenward MG, Roger JH (1997) Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53:983–997PubMedCrossRefGoogle Scholar
  19. Kosina P, Reynolds M, Dixon J, Joshi A (2007) Stakeholder perception of wheat production constraints, capacity building needs, and research partnerships in developing countries. Euphytica 157:475–483CrossRefGoogle Scholar
  20. Krattinger S, Wicker T, Keller B (2009) Map-based cloning of genes in Triticeae (wheat and barley). In: Muehlbauer GJ, Feuillet C (eds) Genetics and genomics of the Triticeae. Springer US, pp 337–357Google Scholar
  21. Lander ES, Green P (1987) Construction of multilocus genetic-linkage maps in humans. Proc Natl Acad Sci USA 84:2363–2367PubMedCrossRefGoogle Scholar
  22. Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95:221–227PubMedCrossRefGoogle Scholar
  23. Liao M, Palta JA, Fillery IRP (2006) Root characteristics of vigorous wheat improve early nitrogen uptake. Aust J Agric Res 57:1097–1107CrossRefGoogle Scholar
  24. Ludwig F, Asseng S (2010) Potential benefits of early vigor and changes in phenology in wheat to adapt to warmer and drier climates. Agr Syst 103:127–136CrossRefGoogle Scholar
  25. Maccaferri M, Sanguineti MC, Corneti S, Ortega JL, Salem MB, Bort J, DeAmbrogio E, del Moral LF, Demontis A, El-Ahmed A, Maalouf F, Machlab H, Martos V, Moragues M, Motawaj J, Nachit M, Nserallah N, Ouabbou H, Royo C, Slama A, Tuberosa R (2008) Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability. Genetics 178:489–511PubMedCrossRefGoogle Scholar
  26. Malosetti M, Ribaut J, Vargas M, Crossa J, van Eeuwijk F (2008) A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica 161:241–257CrossRefGoogle Scholar
  27. Manly KF, Cudmore RH, Meer JM (2001) Map Manager QTX, cross-platform software for genetic mapping. Mamm Genome 12:930–932PubMedCrossRefGoogle Scholar
  28. Martinez O, Curnow RN (1992) Estimating the locations and the sizes of the effects of quantitative trait loci using flanking markers. Theor Appl Genet 85:480–488CrossRefGoogle Scholar
  29. Mason RE, Mondal S, Beecher FW, Pacheco A, Jampala B, Ibrahim AMH, Hays DB (2010) QTL associated with heat susceptibility index in wheat (Triticum aestivum L.) under short-term reproductive stage heat stress. Euphytica 174:423–436CrossRefGoogle Scholar
  30. Mathews KL, Malosetti M, Chapman S, McIntyre L, Reynolds M, Shorter R, van Eeuwijk F (2008) Multi-environment QTL mixed models for drought stress adaptation in wheat. Theor Appl Genet 117:1077–1091PubMedCrossRefGoogle Scholar
  31. McCord AK, Payne RA (2004) Report on the condition of agricultural land in South Australia. Department of Water Land and Biodiversity Conservation, AdelaideGoogle Scholar
  32. McIntyre C, Mathews K, Rattey A, Chapman S, Drenth J, Ghaderi M, Reynolds M, Shorter R (2010) Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions. Theor Appl Genet 120:527–541PubMedCrossRefGoogle Scholar
  33. Oakey H, Verbyla A, Pitchford W, Cullis B, Kuchel H (2006) Joint modeling of additive and non-additive genetic line effects in single field trials. Theor Appl Genet 113:809–819PubMedCrossRefGoogle Scholar
  34. Olivares-Villegas JJ, Reynolds MP, McDonald GK (2007) Drought-adaptive attributes in the Seri/Babax hexaploid wheat population. Funct Plant Biol 34:189–203CrossRefGoogle Scholar
  35. Palta JA, Chen X, Milroy SP, Rebetzke GJ, Dreccer MF, Watt M (2011) Large root systems: are they useful in adapting wheat to dry environments? Funct Plant Biol 38:347–354CrossRefGoogle Scholar
  36. Patterson HD, Thompson R (1971) Recovery of inter-block information when block sizes are unequal. Biometrika 58:545–554CrossRefGoogle Scholar
  37. Paux E, Sourdille P, Salse J, Saintenac C, Choulet F, Leroy P, Korol A, Michalak M, Kianian S, Spielmeyer W, Lagudah E, Somers D, Kilian A, Alaux M, Vautrin S, Berges H, Eversole K, Appels R, Safar J, Simkova H, Dolezel J, Bernard M, Feuillet C (2008) A physical map of the 1-gigabase bread wheat chromosome 3B. Science 322:101–104PubMedCrossRefGoogle Scholar
  38. Paux E, Faure S, Choulet F, Roger D, Gauthier V, Martinant JP, Sourdille P, Balfourier F, Le Paslier MC, Chauveau A, Cakir M, Gandon B, Feuillet C (2010) Insertion site-based polymorphism markers open new perspectives for genome saturation and marker-assisted selection in wheat. Plant Biotechnol J 8:196–210PubMedCrossRefGoogle Scholar
  39. Paux E, Sourdille P, Mackay I, Feuillet C (2011) Sequence-based marker development in wheat: advances and applications to breeding. Biotechnol Adv 30:1071–1088PubMedCrossRefGoogle Scholar
  40. Peleg ZVI, Fahima T, Krugman T, Abbo S, Yakir DAN, Korol AB, Saranga Y (2009) Genomic dissection of drought resistance in durum wheat × wild emmer wheat recombinant inbreed line population. Plant Cell Environ 32:758–779PubMedCrossRefGoogle Scholar
  41. Pinto RS, Reynolds MP, Mathews KL, McIntyre CL, Olivares-Villegas JJ, Chapman SC (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor Appl Genet 121:1001–1021PubMedCrossRefGoogle Scholar
  42. Qu Y–Y, Mu P, Li X-Q, Tian Y-X, Wen F, Zhang H-L, Li Z-C (2008) QTL mapping and correlations between leaf water potential and drought resistance in rice under upland and lowland environments. Acta Agron Sin 34:198–206CrossRefGoogle Scholar
  43. Rebetzke GJ, Bonnett DG, Ellis MH (2012) Combining gibberellic acid-sensitive and insensitive dwarfing genes in breeding of higher-yielding, sesqui-dwarf wheats. Field Crops Res 127:17–25CrossRefGoogle Scholar
  44. Reynolds M, Manes Y, Izanloo A, Langridge P (2009a) Phenotyping approaches for physiological breeding and gene discovery in wheat. An Appl Biol 155:309–320CrossRefGoogle Scholar
  45. Reynolds M, Foulkes MJ, Slafer GA, Berry P, Parry MAJ, Snape JW, Angus WJ (2009b) Raising yield potential in wheat. J Exp Bot 60:1899–1918PubMedCrossRefGoogle Scholar
  46. Richards RA, Watt M, Rebetzke GJ (2007) Physiological traits and cereal germplasm for sustainable agricultural systems. Euphytica 154:409–425CrossRefGoogle Scholar
  47. Scholander PF, Hammel HT, Hemmingsen EA, Bradstreet ED (1964) Hydrostatic pressure and osmotic potential in leaves of mangroves and some other plants. Proc Natl Acad Sci USA 52:119–125PubMedCrossRefGoogle Scholar
  48. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464CrossRefGoogle Scholar
  49. Shi J, Li R, Qiu D, Jiang C, Long Y, Morgan C, Bancroft I, Zhao J, Meng J (2009) Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus. Genetics 182:851–861PubMedCrossRefGoogle Scholar
  50. Smith A, Cullis B, Thompson R (2001) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57:1138–1147PubMedCrossRefGoogle Scholar
  51. Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci 143:449–462CrossRefGoogle Scholar
  52. Tardieu F, Davies WJ (1993) Integration of hydraulic and chemical signalling in the control of stomatal conductance and water status of droughted plants. Plant Cell Environ 16:341–349CrossRefGoogle Scholar
  53. Van Os H, Stam P, Visser RGF, Van Eck HJ (2005) RECORD: a novel method for ordering loci on a genetic linkage map. Theor Appl Genet 112:30–40PubMedCrossRefGoogle Scholar
  54. von Korff M, Grando S, Del Greco A, This D, Baum M, Ceccarelli S (2008) Quantitative trait loci associated with adaptation to Mediterranean dryland conditions in barley. Theor Appl Genet 117:653–669CrossRefGoogle Scholar
  55. Watt M, Kirkegaard JA, Rebetzke GJ (2005) A wheat genotype developed for rapid leaf growth copes well with the physical and biological constraints of unploughed soil. Funct Plant Biol 32:695–706CrossRefGoogle Scholar
  56. Wilhelm EP, Turner AS, Laurie DA (2009) Photoperiod insensitive Ppd-A1a mutations in tetraploid wheat (Triticum durum Desf.). Theor Appl Genet 118:285–294PubMedCrossRefGoogle Scholar
  57. Wu X, Chang X, Jing R (2012) Genetic insight into yield-associated traits of wheat grown in multiple rain-fed environments. PLoS ONE 7:e31249PubMedCrossRefGoogle Scholar
  58. Yoshida T, Nishida H, Zhu J, Nitcher R, Distelfeld A, Akashi Y, Kato K, Dubcovsky J (2010) Vrn-D4 is a vernalization gene located on the centromeric region of chromosome 5D in hexaploid wheat. Theor Appl Genet 120:543–552PubMedCrossRefGoogle Scholar
  59. Zhang XK, Xiao YG, Zhang Y, Xia XC, Dubcovsky J, He ZH (2008) Allelic variation at the vernalization genes Vrn-A1, Vrn-B1, Vrn-D1, and Vrn-B3 in Chinese wheat cultivars and their association with growth habit. Crop Sci 48:458–470CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Julien Bonneau
    • 1
    • 2
  • Julian Taylor
    • 1
  • Boris Parent
    • 1
  • Dion Bennett
    • 1
    • 3
  • Matthew Reynolds
    • 4
  • Catherine Feuillet
    • 2
  • Peter Langridge
    • 1
  • Diane Mather
    • 1
  1. 1.Australian Centre for Plant Functional Genomics and School of Agriculture, Food and Wine, Waite Research InstituteUniversity of AdelaideGlen OsmondAustralia
  2. 2.UMR 1095, Genetics, Diversity and Ecophysiology of CerealsInstitut National de la Recherche Agronomique-Université Blaise PascalClermont Ferrand Cedex 2France
  3. 3.Australian Grain TechnologiesRoseworthyAustralia
  4. 4.International Maize and Wheat Improvement Centre (CIMMYT)Mexico, D.F.Mexico

Personalised recommendations